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Experience Nineleaps’ Virtual Onboarding Success

Category Talent

The current pandemic has come about suddenly and has brought extreme uncertainty around the world. IT companies everywhere have started remote operations, with all their employees working from home. Even though Nineleaps has successfully integrated a remote operations framework, on the management front new challenges arose especially with virtual onboarding.

With our hiring getting more aggressive than before the pandemic, it became vital to discover and implement a virtual process to onboard new talent. After much thought and many discussions, we as a team was able to devise and bring to life a process that has helped us convert this challenge into a new opportunity.

I am writing this article to share my experience in designing and conducting the Virtual Onboarding and Induction program at Nineleaps. One of our beliefs at Nineleaps is the evolution towards the future with active collaboration through commitments. With that belief in our hearts, we transitioned from our traditional onboarding to a more virtual process in welcoming our new hires.

It was in the middle of March when we decided that all our employees will work from home and continue working for customers remotely. The challenge of onboarding arose when new talent started coming in and there was a question about how to welcome them into the family. After much thought, I decided that using a virtual platform would enable remote onboarding easily. The more I thought about it, the more the challenges presented themselves.

We had to design our process in a way so that our new joinees feel welcome in the absence of physical office premises, welcome kits, and the customary meet and greets.
We had to also integrate an easy way to provide them with enough information to start off with.
We also had to discover ways to keep them engaged throughout the virtual onboarding process.
Post Onboarding, we needed a plan to constantly connect with them and give them enough support
Finally, we needed to take in their inputs as feedback and improve the process.

I started researching on the digital platforms for documentation and with the necessary details, I approached my seniors with the proof of concept of the process I had envisioned. After a lengthy discussion, they gave me a thumbs up and we went ahead with it.

We soon finalized on a very user-friendly, open-source platform provide by Zoho. To make sure that our process is radiant and transparent, we developed our own GLEAM Framework as our plan of action.

Greeting: To welcome our new joinees we decided to have a creative welcome note designed by our internal design team that would go as the first thing to their inboxes. Post that, to start with the meet and greets we would have our recruitment team giving notes of welcome to the new joinees post which I would take over to reins in taking the onboarding program forward.

Learning: We reduced our traditional 5-hour program to 3 hours on the first day where we would pass on all the knowledge that the new employees require about the company, processes, policies, etc. They would then be introduced to the tech team and our leaders.

Empathize: We also decided to utilize this opportunity to connect with them on a personal level by going beyond the traditional ‘How are you?’ by understanding their background, current situation and encouraging them to be transparent. This information would help us help them get accommodated better with the organization.

Acquit: We planned their day two in such a way that they could get acquainted with their PoC’s and get a deeper understanding of the next actions that they need to take.

Monitor: We also planned to roll out a questionnaire rolled soon after the Induction program for the new joinees to share their experience on the virtual onboarding, monitoring these inputs to help us to improvise our process for the upcoming batches.

The framework was then approved and we began to onboard our first talents remotely.

On 6th April 2020, we began our first virtual onboarding and induction program with 14 new employees. I remember feeling anxious about the video con-call. However, things settled in soon as there was a synchronicity between me and the employees, the interactions, the new virtual environment, and of course, the new platform. Towards the end of my first Virtual Induction, I remember feeling happy, excited and having a sense of achievement.

We have onboarded 30+ new members to our Nineleaps family. It gave me immense pleasure and a lot of positivity while welcoming the new and fresh faces virtually. I am eager to welcome our next batch.

“The induction was good and informative. It gives the clear picture of the working and culture of the company. I liked being introduced and being able to chat with different people. I was also happy to get interacted by founders as well. So, it was a very nice experience for me.” — Utkarsh Kumar Agarwal (Software Development Engineer II)

Virtual onboarding undoubtedly is a new experience for us however it has made the process more streamlined and quicker by allowing us to continue with our operations even during times like this. Functioning in the current pandemic has made us future-ready in terms of operating remotely and keep the business growing.

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Navigating The Two Major Data Trends in 2024

As the data landscape continues to evolve rapidly, businesses are compelled to stay abreast of emerging trends to maintain competitiveness. In the year 2024, two prominent trends are poised to redefine data analytics: the proliferation of Generative AI and the adoption of modern data contracts. These trends not only reshape how organizations utilize data but also underscore the importance of ethical considerations and robust governance in data management. This article explores these trends in-depth, providing insights into effective strategies for implementation and the implications for businesses navigating the data landscape.Trend #1: The Ascendancy of Generative AIGenerative AI, characterized by its ability to create new content autonomously, has gained significant traction across industries. The advent of large language models (LLMs) has propelled Generative AI into the mainstream, with tech giants like Microsoft, Google, and Meta integrating Generative AI capabilities into their products. As businesses increasingly rely on AI-driven insights, Generative AI is poised to become an indispensable tool for enhancing productivity and driving innovation.Strategy for Effective Implementation:To leverage Generative AI effectively, businesses must develop a comprehensive strategy tailored to their specific needs and objectives. This strategy should encompass several key components:Identifying suitable use cases:Organizations should identify areas where Generative AI can augment existing processes and generate tangible value. Whether it’s automating content creation, personalizing customer experiences, employee training, or optimizing business operations, identifying the right use cases is essential for maximizing ROI.Comprehensive employee training:Implementing Generative AI requires upskilling employees to ensure they can effectively utilize AI tools while adhering to ethical guidelines and best practices. Training programs should cover topics such as data privacy, bias mitigation, and ethical AI usage to foster a culture of responsible AI adoption.Strong data governance:Robust data governance is critical for ensuring the accuracy, security, and ethical usage of AI-generated insights. Organizations must establish clear guidelines and protocols for data collection, storage, and usage to mitigate risks associated with data misuse or bias.Managing costs and licensing:While Generative AI offers immense potential, it also comes with significant costs, both in terms of technology investments and licensing fees. Organizations must develop a cost-effective strategy for scaling AI initiatives while ensuring compliance with budgetary constraints.Balancing automation and human judgment:While AI-driven insights can enhance decision-making processes, it’s essential to strike a balance between automation and human judgment. Human oversight is crucial for interpreting AI-generated insights, identifying biases, and ensuring ethical decision-making.Ethical considerations:As AI becomes increasingly integrated into business operations, organizations must prioritize ethical considerations and accountability. This includes addressing issues related to data privacy, algorithmic bias, and the potential societal impact of AI-driven decisions.Trend #2: Adoption of Modern Data ContractsModern data contracts have emerged as a solution to streamline data usage and sharing, effectively addressing the challenges associated with broken data integrations and communication gaps between application and analytics teams.Structured Data Interactions:Modern data contracts represent a paradigm shift in how organizations manage data interactions. Unlike traditional contracts, which are static and cumbersome to maintain, modern data contracts are dynamic agreements that evolve with changing data requirements and business needs.Integration into workflows:By integrating data contracts into existing workflows and development processes, organizations can ensure seamless data interactions across disparate systems and applications. This integration enables teams to collaborate more effectively, reducing friction and improving data quality and consistency.Implementation Strategies:Implementing modern data contracts requires a strategic approach focused on collaboration, standardization, and automation. Key strategies include:Developing clear standards:Organizations should establish clear standards and guidelines for data contracts, outlining key parameters such as data formats, schemas, and validation rules. These standards help ensure consistency and interoperability across data systems and applications.Instituting change controls:Change management processes are essential for managing versioning and ensuring smooth transitions between data contract iterations. By implementing robust change controls, organizations can minimize disruptions and maintain data integrity throughout the contract lifecycle.Training and tools:Equipping teams with the necessary training and tools is crucial for successful data contract implementation. Training programs should cover topics such as contract management, data governance, and compliance, while tools such as data modeling platforms and contract management software can streamline the contract development and deployment process.As businesses navigate the complexities of the data landscape in 2024, adapting to the rise of Generative AI and modern data contracts is essential for driving innovation and maintaining competitiveness. By developing comprehensive strategies for AI adoption and data governance, organizations can harness the transformative power of Generative AI while ensuring ethical and responsible data usage. Likewise, embracing modern data contracts enables organizations to streamline data interactions, improve collaboration, and enhance data quality and consistency. By embracing these trends and implementing best practices, businesses can unlock new opportunities for growth and success in the digital age.

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The Journey from System Admin to DevOps Superstar

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Performance Testing Trends: Future of Software Optimization

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This approach emphasizes the need for system components to work together harmoniously while assessing their performance individually. The integration of cloud-based environments has become crucial, as cloud computing is an integral part of modern technology infrastructure. Cloud-based environments provide a flexible and reliable platform that enables seamless integration and coordination of various components, ultimately leading to enhanced system performance. It is crucial to prioritize comprehensive performance testing, which involves evaluating individual component performance, managing loads, monitoring in real-time, and debugging, to ensure optimal system performance.Self Service:When adopting the aforementioned trends, it’s essential to consider practical implementation tips for successful outcomes. For instance, performance engineers can use AI-powered tools to analyze performance data more effectively, leading to more accurate and actionable insights. 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